class_docstr: Apply non maxima suppression to filter.
class_docstr_tok: ["Apply","non","maxima","suppression","to","filter","."]
code: def _compute_zero_padding3d(
      kernel_size: tuple[int, int, int]
  ) -> tuple[int, int, int, int, int, int]:
      # TODO: This method is duplicated with some utility function on kornia.filters
      if not isinstance(kernel_size, tuple):
          raise AssertionError(type(kernel_size))
      if len(kernel_size) != 3:
          raise AssertionError(kernel_size)
  
      def pad(x: int) -> int:
          return (x - 1) // 2  # zero padding function
  
      kd, ky, kx = kernel_size  # we assume a cubic kernel
      return pad(kd), pad(kd), pad(ky), pad(ky), pad(kx), pad(kx)
  
file_path: kornia/kornia/geometry/subpix/nms.py
imports: from __future__ import annotations
  import torch
  import torch.nn.functional
  from kornia.core import Module, Tensor, eye, pad, zeros
label_desc: Use image node_id 2 for calling the NonMaximaSuppression3d obj's underlying member method code with example usage: obj._compute_zero_padding3d(kernel_size) and returns: pad,  pad,  pad,  pad,  pad,  pad, unknown